pytorch 计算ConvTranspose1d输出特征大小方式
(编辑：jimmy 日期: 2024/9/22 浏览：2)
问题：如何经过convTransposed1d输出指定大小的特征？
import torch from torch import nn import torch.nn.functional as F conv1 = nn.Conv1d(1, 2, 3, padding=1) conv2 = nn.Conv1d(in_channels=2, out_channels=4, kernel_size=3, padding=1) #转置卷积 dconv1 = nn.ConvTranspose1d(4, 1, kernel_size=3, stride=2, padding=1, output_padding=1) x = torch.randn(16, 1, 8) print(x.size()) x1 = conv1(x) x2 = conv2(x1) print(x2.size()) x3 = dconv1(x2) print(x3.size()) ''' torch.Size([16, 1, 8]) torch.Size([16, 4, 8]) #conv2输出特征图大小 torch.Size([16, 1, 16]) #转置卷积输出特征图大小 '''
#转置卷积 dconv1 = nn.ConvTranspose1d(1, 1, kernel_size=3, stride=3, padding=1, output_padding=1) x = torch.randn(16, 1, 8) print(x.size()) #torch.Size([16, 1, 23]) x3 = dconv1(x) print(x3.size()) #torch.Size([16, 1, 23])
下面两图为演示conv1d，在padding和不padding下的输出特征图大小
不带padding
带padding
补充知识：判断pytorch是否支持GPU加速
如下所示：
print torch.cuda.is_available()
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